# SmoothlyBrokenPowerLaw1D¶

class astropy.modeling.powerlaws.SmoothlyBrokenPowerLaw1D(amplitude=1, x_break=1, alpha_1=-2, alpha_2=2, delta=1, **kwargs)[source] [edit on github]

One dimensional smoothly broken power law model.

Parameters: amplitude : float Model amplitude at the break point. x_break : float Break point. alpha_1 : float Power law index for x << x_break. alpha_2 : float Power law index for x >> x_break. delta : float Smoothness parameter.

Notes

Model formula (with $$A$$ for amplitude, $$x_b$$ for x_break, $$\alpha_1$$ for alpha_1, $$\alpha_2$$ for alpha_2 and $$\Delta$$ for delta):

$f(x) = A \left( \frac{x}{x_b} \right) ^ {-\alpha_1} \left\{ \frac{1}{2} \left[ 1 + \left( \frac{x}{x_b}\right)^{1 / \Delta} \right] \right\}^{(\alpha_1 - \alpha_2) \Delta}$

The change of slope occurs between the values $$x_1$$ and $$x_2$$ such that:

$\log_{10} \frac{x_2}{x_b} = \log_{10} \frac{x_b}{x_1} \sim \Delta$

At values $$x \lesssim x_1$$ and $$x \gtrsim x_2$$ the model is approximately a simple power law with index $$\alpha_1$$ and $$\alpha_2$$ respectively. The two power laws are smoothly joined at values $$x_1 < x < x_2$$, hence the $$\Delta$$ parameter sets the “smoothness” of the slope change.

The delta parameter is bounded to values greater than 1e-3 (corresponding to $$x_2 / x_1 \gtrsim 1.002$$) to avoid overflow errors.

The amplitude parameter is bounded to positive values since this model is typically used to represent positive quantities.

Examples

import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models

x = np.logspace(0.7, 2.3, 500)
f = models.SmoothlyBrokenPowerLaw1D(amplitude=1, x_break=20,
alpha_1=-2, alpha_2=2)

plt.figure()
plt.title("amplitude=1, x_break=20, alpha_1=-2, alpha_2=2")

f.delta = 0.5
plt.loglog(x, f(x), '--', label='delta=0.5')

f.delta = 0.3
plt.loglog(x, f(x), '-.', label='delta=0.3')

f.delta = 0.1
plt.loglog(x, f(x), label='delta=0.1')

plt.axis([x.min(), x.max(), 0.1, 1.1])
plt.legend(loc='lower center')
plt.grid(True)
plt.show()


(png, svg, pdf)

Attributes Summary

 alpha_1 alpha_2 amplitude delta input_units This property is used to indicate what units or sets of units the evaluate method expects, and returns a dictionary mapping inputs to units (or None if any units are accepted). param_names x_break

Methods Summary

 evaluate(x, amplitude, x_break, alpha_1, …) One dimensional smoothly broken power law model function fit_deriv(x, amplitude, x_break, alpha_1, …) One dimensional smoothly broken power law derivative with respect to parameters

Attributes Documentation

alpha_1
alpha_2
amplitude
delta
input_units

This property is used to indicate what units or sets of units the evaluate method expects, and returns a dictionary mapping inputs to units (or None if any units are accepted).

Model sub-classes can also use function annotations in evaluate to indicate valid input units, in which case this property should not be overriden since it will return the input units based on the annotations.

param_names = ('amplitude', 'x_break', 'alpha_1', 'alpha_2', 'delta')
x_break

Methods Documentation

static evaluate(x, amplitude, x_break, alpha_1, alpha_2, delta)[source] [edit on github]

One dimensional smoothly broken power law model function

static fit_deriv(x, amplitude, x_break, alpha_1, alpha_2, delta)[source] [edit on github]

One dimensional smoothly broken power law derivative with respect to parameters